If anyone wants to improve the usability, data files are:
http://www.bloomberg.com/graphics/2016-who-marries-whom/job-...
http://www.bloomberg.com/graphics/2016-who-marries-whom/pair...
http://www.bloomberg.com/graphics/2016-who-marries-whom/job-...
http://www.bloomberg.com/graphics/2016-who-marries-whom/pair...
13.9% Physicians and Surgeons
10.7% Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders
10.1% none
9.7% Farmers, Ranchers, and Other Agricultural Managers
9.7% Lawyers, and judges, magistrates, and other judicial workers
9.5% Miscellaneous Personal Appearance Workers
9.3% Veterinarians
8.8% Dentists
8.5% Miscellaneous agricultural workers including animal breeders
8.4% Postsecondary Teachers
7.6% Software Developers, Applications and Systems Software
7.6% Health Diagnosing and Treating Practitioners, All Other
7.5% Optometrists
7.3% Chiropractors
7.2% Pharmacists
6.7% Elementary and Middle School Teachers
6.4% Food Service Managers
6.2% Agricultural and Food Scientists
6.1% Physical Therapists
6.1% Gaming Services Workers
6.0% Upholsterers
5.9% Communications Equipment Operators, All Other
5.8% Air Traffic Controllers and Airfield Operations Specialists
5.8% Physical Scientists, All Other
5.8% Nurse Anesthetists
5.5% Chief executives and legislators
5.5% Real Estate Brokers and Sales Agents
5.4% Clergy
5.2% Marine Engineers and Naval Architects
5.0% Psychologists
4.9% Lodging Managers
4.8% First-Line Supervisors of Retail Sales Workers
4.7% Miscellaneous Managers, Including Funeral Service Managers and Postmasters and Mail Superintendents
4.7% Medical Scientists, and Life Scientists, All Other
4.6% Secondary School Teachers
4.0% Textile Knitting and Weaving Machine Setters, Operators, and Tenders
4.0% Podiatrists
4.0% News Analysts, Reporters and Correspondents
4.0% Sewing Machine Operators
3.9% Bailiffs, Correctional Officers, and Jailers
3.9% First-Line Supervisors of Personal Service Workers
3.8% Tailors, Dressmakers, and Sewers
3.8% Economists
3.8% Musicians, Singers, and Related Workers
3.8% Environmental Scientists and Geoscientists
3.8% Property, Real Estate, and Community Association Managers
3.7% Insurance Sales Agents
3.5% Agricultural Inspectors
3.3% Butchers and Other Meat, Poultry, and Fish Processing Workers
3.2% Morticians, Undertakers, and Funeral DirectorsDoctors for instance are well known for pairing up during the residency grind since it dramatically drops their interactions with anyone outside of their residency program and also occurs during their late 20's. A perfect storm.
Physicians are still up there, but some other occupations have also entered, such as "Farmers, Ranchers"
Doctors are prestige seekers who see marrying a software programmer as a step down.
many fish in the sea, no reason to be frustrated.
(Doctor here. Married to probably the only female doctor who is not hard to live with ;))
As a generalisation though, many (or most) doctors:
- Have lousy work hours.
- Have a lot of work-related stress and are therefore often quite moody and irritable.
- Often have poor social skills, and can be particularly bad at resolving conflicts.
- Have a tendency towards narcissism and can therefore be quite high maintenance.
- Don't have an off-switch when it comes to work, so may bore you to death with work-related stories and complaints.
It looks like I forgot to check in the R script used to process the .dat files to my gist https://gist.github.com/1wheel/a8e65b1576b750c5e3e3, but I'll add it when I get home.
We also tried to use salary info like the magazine version https://twitter.com/adamrpearce/status/697844614754123776 did, but had some problems with slighty different occuptation categories: http://www.bloomberg.com/graphics/2016-who-marries-whom/sala...
Interested to see what people come up with! This is a super interesting, rich data set that deserves much more than what I've done.
$('.job-text').css({color: 'black'})
1,1010,,,1.53E+07
where sex_sp and occ_sp is omitted. Also, how to interpret the first entry in the job-name.csv:
none,none,0,none
I'm currently inclined to simply ignore these in my analysis.